Data Pipelines & ETL
Every impressive agent demo sits on an unimpressive truth: something has to fetch, clean, dedupe, and normalize the data first — from the ERP that exports cursed CSVs, the SharePoint nobody has governed since 2019, and the vendor API with rate limits designed by a sadist. That plumbing is where AI projects actually succeed or fail.
It's also our home turf. We build pipelines that pull from your real systems, turn PDFs and scans into structured text worth embedding, enforce schemas at the boundary, and keep indexes fresh incrementally instead of re-processing the world nightly. Idempotent, monitored, and boring — so the exciting layer on top has something true to reason over.
Source ingestion
Connectors for databases, SaaS APIs, file shares, and the legacy systems vendors pretend don't exist.
Document processing
PDF, scan, and OCR pipelines that turn document chaos into clean, structured, embeddable text.
Normalization and quality gates
Schema enforcement, deduplication, and validation at the boundary — garbage stops at the door.
Incremental freshness
Change-data-capture and delta processing so your agents read today's truth, not last month's.
Pipeline observability
Lineage, failure alerts, and data-quality dashboards — you know when something's wrong before the agent does.
Our data is a mess. Do we need to fix it before starting with AI?
No — you need to fix the slice that matters. Boiling the data-governance ocean is how initiatives die. We identify the sources the first workflow actually needs, build quality gates for those, and expand from there. Perfect data is a mirage; sufficient data is a milestone.
How do you keep the agent's knowledge current?
Incremental pipelines: change detection on sources, delta processing, and freshness SLOs monitored like uptime. If a policy document changes at 9 a.m., the agent answers from the new version the same day — not after the quarterly re-index.
Can you work with our existing data stack?
Yes — we build on what you have. Airflow, dbt, Kafka, plain Postgres and cron: all fine. Forward deployment means fitting your stack and your team, not arriving with a rip-and-replace platform proposal.
Put Data Pipelines & ETL to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
Book a deployment →